Switchable Vesicles Formed by Diblock Random Copolymers with Tunable pH- and Thermo-Responsiveness
Bibliographic record
Abstract
The thermo-responsiveness of polymers in aqueous media can be tuned by the choice of comonomers used in the synthesis of block copolymers made of random sequences of the same comonomers but of different molar ratios. The same synthetic approach may be applied to other stimuli and we have made diblock random copolymers with both pH- and thermo-responsiveness and studied the formation of vesicles whose membrane core and coronas may be inverted in aqueous media. Sequential reversible addition-fragmentation chain transfer (RAFT) polymerization was used to prepare well-defined block copolymers in the form of AnBm-b-ApCq, where A, B, and C are N-n-propylacrylamide (nPA), 2-(diethylamino)ethyl methacrylate (DEAEMA), and N-ethylacrylamide (EA), respectively. This polymer shows interesting "schizophrenic" behavior in aqueous solutions. Both blocks are thermo-responsive, and one block is pH-responsive in which the tertiary amine group of DEAEMA may be protonated at a lower pH. A molecularly dissolved polymer is obtained at neutral pH and ambient temperature. At pH 7 and 37 °C, the polymer self-assembles into vesicles with the poly(nPA0.8-co-EA0.2) block as the membrane core (mean hydrodynamic diameter of the vesicles Dh = 148 nm). In an alkaline medium (pH 10) at 25 °C, the membrane core and the coronas of the vesicles are inverted with poly(nPA0.8-co-DEAEMA0.2) block forming the core (Dh = 60 nm). In addition, two-step phase transitions are observed in both alkaline and neutral solutions corresponding to the cloud points of the individual blocks. Here, the random nature of the blocks allows fine-tuning the thermo-responsiveness based solely on lower critical solution temperatures and its combination with pH-sensitivity provides vesicles with switchable membrane core and corona in aqueous solution.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".